Terrestrial carbon dynamics and economic valuation of ecosystem service for land use management in the Mediterranean region

Ecological Informatics, Год журнала: 2024, Номер 81, С. 102570 - 102570

Опубликована: Март 26, 2024

Land use change is a significant cause of land degradation, resulting in the removal carbon and its transfer to hydrosphere or atmosphere, which can have detrimental impact on essential ecosystem functions services. In contrast, rural landscapes offer both ecological economic benefits people due their storage properties. This benefit often evaluated within framework Ecosystem Services (ES). Rural unique structures The aim this study provide an quantification sequestration as service introduce carbon-based landscape management. enables integration strategies policies by generating quantitative outputs for plans that not yet been included spatial planning hierarchy. Upper Seyhan Basin (USB), located Eastern Mediterranean coast Turkiye, was modelled using spatiotemporal valuation approaches assess terrestrial context. mapped four main components landscape: (i) above-ground biomass, (ii) below-ground (iii) Soil Organic Carbon (SOC), (iv) dead organic matter carbon. future cover 2055 Multilayer Perceptron (MLP)-Markov Chain (MC) algorithm based "Business-As-Usual" (BAU) scenario determine sequestration. Social Cost (SCC) estimated Turkiye Regional Integrated Model Climate Economy (RICE) economically model final stage study, modelling outputs, including pools/sinks, projected dynamics, SCC components, were used Valuation Tradeoffs (InVEST) map dynamics at regional level. paper demonstrates role geospatial proposes new design logic considers inputs global climate appropriate format management bridging interface between earth sciences planning. results indicate that, area provided 70,266 TgC 2014 with value USD 222,853,170. For 2014–2025 projection, 1.042 valued 6,234,137. 2014–2055 projection 3.185 58,745,265.

Язык: Английский

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A machine learning approach to mapping suitable areas for forest vegetation in the eThekwini municipality

Remote Sensing Applications Society and Environment, Год журнала: 2024, Номер 35, С. 101208 - 101208

Опубликована: Апрель 23, 2024

Driven by climate change, global forests are undergoing significant transformations in growth, ecology, and distribution, necessitating informed restoration conservation strategies, particularly the eThekwini Municipality where anthropogenic activities exacerbate these trends. Modelling current forest suitability (2023) utilized bioclimatic variables from WorldClim dataset, alongside elevation slope Shuttle Radar Topography Mission (SRTM) with remote sensing data acquired Landsat 9 Sentinel 2A. Future (2021 – 2040) was projected also using two Global Climate Models (GCMs) under four Shared Socioeconomic Pathway (SSP)-based Representative Concentration (RCP) scenarios. Employing Random Forests (RF), Light Gradient Boosting (LightGBM), Artificial Neural Networks (ANN), processing carried out Google Earth Engine (GEE), QGIS Python, model accuracy primarily assessed Receiver Operating Characteristic (ROC) curves Area Under ROC Curve (AUC). LightGBM demonstrated superior performance, achieving AUCs of 96.88% 93.75% for future mapping, respectively, annual precipitation vegetation changes identified as crucial variables. Currently, 30% municipality's land is deemed suitable, concentrated central region. projections highlight mountainous north-western region most notably SSP370 scenario a suitable area 63%. Strategic recommendations include prioritizing reforestation efforts, engaging private landowners, exploring urban opportunities, implementing continuous monitoring adaptive management, thereby enhancing carbon sequestration, biodiversity conservation, ecosystem resilience. This study provides valuable insights decision-making despite inherent uncertainties.

Язык: Английский

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Monitoring the rehabilitation process of the windthrow area using UAS images and performance comparison of Sentinel-2A based different vegetation indexes

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Янв. 22, 2025

Abstract Windthrows significantly disrupt forest ecosystems, impacting biotic community life cycles. To ensure the reformation of ecosystem chain, it is essential to rehabilitate windthrow area as soon possible. Therefore, mandotory determine success rehabilitation processes. In this study, process that occurred in Düzce Tatlıdere Forest District (DTFD) was identified using vegetation indices calculated from Unmanned Aircraft System (UAS) images and Sentinel-2A satellite between 2017 2022. The Normalized Difference Red Edge Index (NDRE), Plant Senescence Reflectance (PSRI), Vegetation (NDVI) were images, most successful index for detecting reforested areas identified. UAS used create training data, data classify with Random (RF) algorithm. classification’s accuracy assessed Kappa Coefficient Overall Accuracy (%). Results showed NDVI had lowest both years, whereas NDRE succesfully detected borders. PSRI monitoring progress This he effectiveness limitations NDRE, have been detected, important bands determined based on results RF classification. study pioneering use detect post-windthrow.

Язык: Английский

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Linking remotely sensed growth-related canopy attributes to interannual tree-ring width variations: A species-specific study using Sentinel optical and SAR time series

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2025, Номер 221, С. 347 - 362

Опубликована: Фев. 20, 2025

Язык: Английский

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Modelling soil organic carbon at multiple depths in woody encroached grasslands using integrated remotely sensed data

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(3)

Опубликована: Март 1, 2025

Abstract Woody plants encroachment into grasslands has considerable hydrological and biogeochemical consequences to grassland soils that include altering the Soil Organic Carbon (SOC) pool. Consequently, continuous SOC stock assessment evaluation at deeper soil depths of woody encroached is essential for informed management monitoring phenomenon. Due high litter biomass deep root structures, landscapes have been suggested alter accumulation layers; however, extent which sequester within localized protected still poorly understood. Remote sensing methods techniques recently popular in analysis due better spatial spectral data properties as well availability affordable eco-friendly data. In this regard, study sought quantify various (30 cm, 60 100 cm) a woody-encroached by integrating Sentinel-1 (S1), Sentinel-2 (S2), PlanetScope (PS) satellite imagery, topographic variables. was quantified from 360 field-collected samples using loss-On-Ignition (LOI) method distribution across Bisley Nature Reserve modelled employing Random Forest (RF) algorithm. The study’s results demonstrate integration variables, Synthetic Aperture Radar (SAR), effectively stocks all investigated depths, with R 2 values 0.79 RMSE 0.254 t/ha. Interestingly, were higher 30 cm compared depths. horizontal reception (VH), Slope, Topographic Weightiness Index (TWI), Band 11 vertical (VV) optimal predictors landscapes. These highlight significance RF model variables accurate modelling ecosystems. findings are pivotal developing cost-effective labour-efficient system appropriate habitats.

Язык: Английский

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Optimising forest rehabilitation and restoration through remote sensing and machine learning: Mapping natural forests in the eThekwini Municipality

Remote Sensing Applications Society and Environment, Год журнала: 2024, Номер 36, С. 101335 - 101335

Опубликована: Авг. 28, 2024

Forests are crucial in delivering ecosystem services that underpin human well-being and biodiversity conservation. However, these vital ecosystems threatened by forest degradation rapid urbanisation. This study addresses this challenge proposing a comprehensive framework for mapping natural forests at the municipal scale. The integrates remote sensing techniques with machine learning algorithms to provide valuable insights into extent of within eThekwini Municipality. utilised Landsat 7, 8, 9 satellite imagery analyse map historical current distribution forests. Five spectral indices, namely, Normalized Differential Vegetation Index (NDVI), Green Difference (GNDVI), Chlorophyll (CIG), Enhanced (EVI), Index-2 (EVI-2), which were calculated from bands, employed analysis. Light Gradient Boosting Machine (LightGBM), Categorical (CatBoost), Extreme (XGBoost) used model distribution. Accuracy was assessed through confusion matrices, Receiver Operating Characteristic (ROC) Curves, area under ROC curve (AUC), F1 scores. LightGBM achieved highest overall accuracy (90.76%), followed CatBoost (89.56%) XGBoost (84.34%). also obtained best score (90.76%). These findings highlight LightGBM's effectiveness classifying forests, making it preferred classifications based on 7 significantly underestimated area, whereas 8 data revealed an increase 2015 2023. will guide effective targeted rehabilitation restoration efforts, ensuring preservation enhancement services.

Язык: Английский

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Aquatic vegetation mapping with UAS-cameras considering phenotypes

Ecological Informatics, Год журнала: 2024, Номер 81, С. 102624 - 102624

Опубликована: Май 6, 2024

Aquatic vegetation species at the genus level in an oxbow lake were identified Hungary based on a multispectral Uncrewed Aerial System (UAS) survey within elongated area of Tisza River under continental climate. Seven and 13 classes discriminated using three different classification methods (Support Vector Machine [SVM], Random Forest [RF], Multivariate Adaptive Regression Splines [MARS]) input data ten combinations: original spectral bands, indices, Digital Surface Model (DSM), Haralick texture indices. We achieved high (97.1%) overall accuracies (OAs) by applying SVM classifier, but RF performed only <1% worse, as it was represented first places rank before MARS. The highest (>84% OA) obtained most important variables derived Recursive Feature Elimination (RFE) method. best required DSM variable. poorest performance belonged to model that used indices or On class level, Stratoites aloides exhibit lowest degree separability compared other classes. Accordingly, we recommend supplementary for classifications besides example, DSM, spectral, these significantly improve proper combinations variables.

Язык: Английский

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Modelling Soil Organic Carbon at Multiple Depths in Woody Encroached Grasslands Using Integrated Remotely Sensed Data

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 3, 2024

Woody plants encroachment into grasslands has considerable hydrological and biogeochemical consequences to grassland soils that include altering the Soil Organic Carbon (SOC) pool. Consequently, continuous SOC stock assessment evaluation at deeper soil depths of woody encroached is essential for informed management monitoring phenomenon. Due high litter biomass deep root structures, landscapes have been suggested alter accumulation layers, however, level which sequestrate within localized protected still poorly understood. Remote sensing methods techniques recently popular in analysis due better spatial spectral data properties as well availability affordable eco-friendly data. In this regard, study sought quantify various (30 cm, 60 100 cm) a woody-encroached by integrating Sentinel-1 (S1), Sentinel-2 (S2), PlanetScope (PS) satellite imagery, topographic variables. was quantified from 360 field-collected samples using loss-On-Ignition (LOI) method distribution across Bisley Nature Reserve modelled through employing Random Forest (RF) algorithm. The study’s results demonstrate integration variables, Synthetic Aperture Radar (SAR), effectively stocks all investigated depths, with R² values 0.79 RMSE 0.254 t/ha. Interestingly, established be 30 cm compared depths. horizontal reception (VH), Slope, Topographic Weightiness Index (TWI), Band 11 vertical (VV) were optimal predictors landscapes. These highlight significance RF model variables accurate modelling ecosystems. findings are pivotal developing cost-effective labour-efficient system appropriate habitats

Язык: Английский

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